We present a machine vision system in which segmentation is computed in conjunction with a structural description of objects in the scene. It is assumed that contrast edges capture all relevant object information. The principles which dictate how edge features are grouped to infer objects are based upon detecting SYMMETRICAL ENCLOSING edge configurations. These are detected using ANNULAR OPERATORS applied at multiple scales to edge data which have been extracted at multiple scales from a gray level image. The subsequent grouping of symmetry points results in a set of PARTS which make it possible to identify the LOCATION of objects within an image. These parts are used as a basis for constructing coarse graph-based DESCRIPTORS for the PERCEPTUALLY SIGNIFICANT objects found in the scene. Results are presented to illustrate the method's performance on several images.>

It is essential that autonomous robots be able to locate and identify objects in their environment. A novel approach for visually extracting such object information from images is presented. Annular operators are used to identify existing symmetric relationships between sets of edge elements. Operators are applied at multiple scales to edge data which have been extracted at multiple scales from a gray-scale image. From the resulting symmetry points, the authors identify a set of object parts in the scene. These are used as the basis for constructing coarse graph-based object descriptors. Preliminary results are presented to illustrate the approach using natural image data.